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Title: Recurrence outcome of lymph node ratio in gastric cancer after underwent curative resection: A retrospective cohort study
Authors: Chairat Supsamutchai
Chumpon Wilasrusmee
Jakrapan Jirasiritham
Teerawut Rakchob
Songpol Phosuwan
Tanet Chatmongkonwat
Pattawia Choikrua
Tharin Thampongsa
Mahidol University
Keywords: Medicine
Issue Date: 1-Jun-2020
Citation: Annals of Medicine and Surgery. Vol.54, (2020), 57-61
Abstract: © 2020 The Authors Introduction: D2 dissection has been regarded as the standard procedure for locally advanced gastric cancer (GC). Number of lymph nodes (LN) harvested is an important factor for accurate staging. The number of LN retrieved and the metastasis LN status are also important factors to determine the prognosis. This study aims to evaluate whether lymph node ratio (LNR) could be a prognostic indicator of GC patients following curative resection. Patients and methods: Single center retrospective cohort study of GC patients underwent curative resection from January 1995 to December 2016 was conducted. The patients were categorized into 3 groups based on LNR (0.00–0.35, >0.35–0.75, and >0.75–1.00) and 2 groups based on number of LN retrieved (<15 and ≥ 15). Kaplan-Meier method was used to estimate recurrence-free survival. Cox-regression were used to determine the association between LNR/other factors and the disease recurrence. Results: One-hundred fifty-three patients were included in analysis. Univariate analysis showed that LNR >0.35, pathologic LN stages (pN) 2–3, higher number of LN metastasis, and TNM stage III were significantly recurrence risk factors. After adjusting for several covariates, LNR >0.35 still was significant predictor (adjusted HR [95%CI], 8.53 [1.97, 36.86]; p = 0.004) while number of LN retrieved or number of metastasis LN were not. Conclusion: LNR could be a strong indicator for the recurrence of GC after curative resection while the number of LN retrieved or metastasis did not predict the recurrence. Future studies, such as prospective studies, are needed to confirm and identify the optimum LNR cut-off.
ISSN: 20490801
Appears in Collections:Scopus 2020

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